Completely non-invasive prediction of IDH mutation status based on preoperative native CT images
Abstract The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioact...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-77789-6 |
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| author | Manfred Musigmann Melike Bilgin Sabriye Sennur Bilgin Hermann Krähling Walter Heindel Manoj Mannil |
| author_facet | Manfred Musigmann Melike Bilgin Sabriye Sennur Bilgin Hermann Krähling Walter Heindel Manoj Mannil |
| author_sort | Manfred Musigmann |
| collection | DOAJ |
| description | Abstract The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date. In our study, we show that radiomics-based machine learning allows to determine IDH mutation status based on preoperative native CT images both with very high accuracy and completely non-invasively. Based on independent test data, we are able to correctly identify 91.1% of cases with an IDH mutation. Our final model, containing only six features, exhibits a high area under the curve of 0.847 and an excellent area under the precision-recall curve of 0.945. In the future, such models may be used for a completely non-invasive prediction of important genetic markers, potentially allowing treating physicians to reduce the number of biopsies and speed up further treatment planning. |
| format | Article |
| id | doaj-art-57d82ba3e5ee4a4ba1241daeed0f34e7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-57d82ba3e5ee4a4ba1241daeed0f34e72024-11-10T12:19:45ZengNature PortfolioScientific Reports2045-23222024-11-0114111010.1038/s41598-024-77789-6Completely non-invasive prediction of IDH mutation status based on preoperative native CT imagesManfred Musigmann0Melike Bilgin1Sabriye Sennur Bilgin2Hermann Krähling3Walter Heindel4Manoj Mannil5University Clinic for Radiology, University Münster and University Hospital MünsterUniversity Clinic for Radiology, University Münster and University Hospital MünsterUniversity Clinic for Radiology, University Münster and University Hospital MünsterUniversity Clinic for Radiology, University Münster and University Hospital MünsterUniversity Clinic for Radiology, University Münster and University Hospital MünsterUniversity Clinic for Radiology, University Münster and University Hospital MünsterAbstract The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date. In our study, we show that radiomics-based machine learning allows to determine IDH mutation status based on preoperative native CT images both with very high accuracy and completely non-invasively. Based on independent test data, we are able to correctly identify 91.1% of cases with an IDH mutation. Our final model, containing only six features, exhibits a high area under the curve of 0.847 and an excellent area under the precision-recall curve of 0.945. In the future, such models may be used for a completely non-invasive prediction of important genetic markers, potentially allowing treating physicians to reduce the number of biopsies and speed up further treatment planning.https://doi.org/10.1038/s41598-024-77789-6GliomaIDH mutation statusMachine learningArtificial intelligenceNeuroimagingComputed tomography (CT) |
| spellingShingle | Manfred Musigmann Melike Bilgin Sabriye Sennur Bilgin Hermann Krähling Walter Heindel Manoj Mannil Completely non-invasive prediction of IDH mutation status based on preoperative native CT images Scientific Reports Glioma IDH mutation status Machine learning Artificial intelligence Neuroimaging Computed tomography (CT) |
| title | Completely non-invasive prediction of IDH mutation status based on preoperative native CT images |
| title_full | Completely non-invasive prediction of IDH mutation status based on preoperative native CT images |
| title_fullStr | Completely non-invasive prediction of IDH mutation status based on preoperative native CT images |
| title_full_unstemmed | Completely non-invasive prediction of IDH mutation status based on preoperative native CT images |
| title_short | Completely non-invasive prediction of IDH mutation status based on preoperative native CT images |
| title_sort | completely non invasive prediction of idh mutation status based on preoperative native ct images |
| topic | Glioma IDH mutation status Machine learning Artificial intelligence Neuroimaging Computed tomography (CT) |
| url | https://doi.org/10.1038/s41598-024-77789-6 |
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